Vehicle energy consumption efficiency learning in the energy domain

ABSTRACT

A method according to an exemplary aspect of the present disclosure includes, among other things, learning an energy consumption efficiency of a vehicle in an energy domain by periodically filtering a ratio of a distance traveled to an energy consumed. The learning step is executed by a control module configured to monitor the energy consumption efficiency.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/282,352, which was filed on May 20, 2014.

TECHNICAL FIELD

This disclosure relates to a vehicle, and more particularly, but notexclusively, to energy consumption efficiency learning in the energydomain via an energy triggered filter.

BACKGROUND

An energy source in necessary to provide power for propelling a vehicle.For example, petroleum based products, such as gasoline or diesel, arethe main energy source of conventional vehicles that include internalcombustion engines. On the other hand, electrified vehicles are knownthat utilize one or more electric machines and in some cases an enginethat can be used either individually or in combination to propel thevehicle. A high voltage battery typically acts as one energy source forpowering such electric machines.

It may be beneficial to calculate the energy use of a vehicle in orderto display a variety of end use features to the vehicle driver. Forexample, an energy consumption rate may be monitored and used to predicta distance to empty (DTE) value which can be displayed to the vehicledriver to provide information for trip planning, minimizing drivingcosts, evaluating vehicle performance, etc. One current method used tomonitor energy use of a vehicle is a time domain approach. However,other approaches may provide improvements to existing methods.

SUMMARY

A method according to an exemplary aspect of the present disclosureincludes, among other things, learning an energy consumption efficiencyof a vehicle in an energy domain by periodically filtering a ratio of adistance traveled to an energy consumed. The learning step is executedby a control module configured to monitor the energy consumptionefficiency.

In a further non-limiting embodiment of the foregoing method, thelearning step includes monitoring the energy consumption andperiodically adapting an energy consumption efficiency prediction.

In a further non-limiting embodiment of either of the foregoing methods,the learning step includes integrating a speed of the vehicle to obtainthe distance traveled and integrating a power consumption of the vehicleto obtain the energy consumed.

In a further non-limiting embodiment of any of the foregoing methods,the filtering step is selectively performed after the integrating steps.

In a further non-limiting embodiment of any of the foregoing methods,the method includes multiplying the ratio by a filter constant.

In a further non-limiting embodiment of any of the foregoing methods,the method includes updating the energy consumption efficiency if theenergy consumed since a prior integrator reset is greater than or equalto an energy consumed threshold.

In a further non-limiting embodiment of any of the foregoing methods,the method includes using a prior energy consumption efficiency if theenergy consumed since the prior integrator reset is not greater than orequal to the energy consumed threshold.

In a further non-limiting embodiment of any of the foregoing methods,the method includes resetting a vehicle speed integral and a powerconsumption integral if the energy consumed since a prior integratorreset is greater than or equal to an energy consumed threshold.

In a further non-limiting embodiment of any of the foregoing methods,the method includes utilizing the energy consumption efficiency tocalculate at least one end use feature associated with the vehicle.

In a further non-limiting embodiment of any of the foregoing methods,the at least one end use feature is a distance to empty estimation.

In a further non-limiting embodiment of any of the foregoing methods,the at least one end use feature is an instantaneous consumption ratedisplay.

In a further non-limiting embodiment of any of the foregoing methods,the at least one end use feature is at least one of an averageconsumption rate/efficiency over a trip odometer, a running averageconsumption rate/efficiency for a current key cycle and a lifetimerunning average consumption rate/efficiency.

In a further non-limiting embodiment of any of the foregoing methods,the at least one end use feature is at least one of a grade estimationand a towing load estimation.

In a further non-limiting embodiment of any of the foregoing methods,the at least one end use feature is an ECO-routing or an ECO-coachingfeature.

In a further non-limiting embodiment of any of the foregoing methods,the method includes suspending the learning step during steep gradedriving conditions.

A vehicle according to another exemplary aspect of the presentdisclosure includes, among other things, a transmission, a propulsiondevice coupled to wheels by the transmission, an energy sourceconfigured to power the propulsion device, and a control module inelectrical communication with the propulsion device and the energysource. The control module is configured to learn an energy consumptionefficiency associated with the vehicle in an energy domain.

In a further non-limiting embodiment of the foregoing vehicle, thecontrol module is configured to update the energy consumption efficiencyeach time a predefined amount of energy is consumed by the propulsiondevice.

In a further non-limiting embodiment of either of the foregoingvehicles, the control module is configured to suspend learning theenergy consumption efficiency of the vehicle during steep grade drivingconditions.

In a further non-limiting embodiment of any of the foregoing vehicles,the control module is configured to integrate a speed of the vehicle toobtain a distance travelled and integrate a power consumption of thevehicle to obtain an energy consumed.

In a further non-limiting embodiment of any of the foregoing vehicles,the control module is configured to filter a ratio of the distancetravelled to the energy consumed.

The embodiments, examples and alternatives of the preceding paragraphs,the claims, or the following description and drawings, including any oftheir various aspects or respective individual features, may be takenindependently or in any combination. Features described in connectionwith one embodiment are applicable to all embodiments, unless suchfeatures are incompatible.

The various features and advantages of this disclosure will becomeapparent to those skilled in the art from the following detaileddescription. The drawings that accompany the detailed description can bebriefly described as follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates a powertrain of a vehicle.

FIG. 2 schematically illustrates a control strategy for learning anenergy consumption efficiency of a vehicle in an energy domain.

FIG. 3 illustrates a control strategy according to a second embodimentof this disclosure.

DETAILED DESCRIPTION

This disclosure relates to vehicle energy consumption efficiencylearning. The vehicle may be an electrified vehicle, a conventionalvehicle, or any other vehicle type. In one embodiment, the energyconsumption efficiency learning is performed in an energy domain via anenergy triggered filter. The learned energy consumption efficiency maybe used to calculate one or more end use features associated with thevehicle. These and other features are discussed in greater detailherein.

FIG. 1 schematically illustrates a vehicle 10. This disclosure isapplicable to any type of vehicle. For example, the vehicle 10 could bea conventional vehicle that is powered by an internal combustion engine,or could be an electrified vehicle that utilizes one or more electricmachines in addition to, or as an alternative to, an engine.

The exemplary vehicle 10 includes a powertrain 12. The powertrain 12 mayinclude a propulsion device 14 and a transmission 16 that is selectivelydriven by the propulsion device 14. The propulsion device 14 may beemployed as an available drive source for the vehicle 10. For example,the propulsion device 14 could include an engine for a conventionalvehicle, or an electric machine (i.e., an electric motor, a generator ora combined motor/generator) for an electrified vehicle.

The transmission 16 may include a gearbox having multiple gear sets (notshown) that are selectively operated using different gear ratios byselective engagement of friction elements such as clutches and brakes(not shown) to establish the desired multiple discrete or step driveratios. The friction elements are controllable through a shift schedulethat connects and disconnects certain elements of the gear sets tocontrol the ratio between a transmission input shaft 19 and atransmission output shaft 20. The transmission 16 may alternatively becontrolled to achieve an infinite number of ratios. These ratios can beachieved through mechanical reconfiguration as in a continuouslyvariable transmission (CVT) or by electrical coordinate of the speeds ofelectric machines as in an electrically continuously variabletransmission (eCVT). The transmission 16 may be automatically shiftedfrom one ratio to another based on various vehicle and ambient operatingconditions by an associated control module 28. The transmission 16 thenprovides powertrain output torque to the transmission output shaft 20.The transmission output shaft 20 may be connected to a differential 22.The differential 22 drives a pair of wheels 24 via respective axles 26that are connected to the differential 22 to propel the vehicle 10.

An energy source 18 may supply power to the propulsion device 14. Theenergy source 18 may be a fuel system if the propulsion device 14 is anengine or a high voltage battery if the propulsion device 14 is anelectric machine. For example, an engine is configured to consume fuel(i.e., gasoline, diesel, etc.) to produce a motor output, whereas thehigh voltage battery is configured to output and receive electricalenergy that is consumed by the electric machine to produce a motoroutput.

The powertrain 12 of the vehicle 10 may additionally include anassociated control module 28. While schematically illustrated as asingle module, the control module 28 may be part of a larger controlsystem and may be controlled by various other controllers throughout thevehicle 10, such as a vehicle system controller (VSC) that includes apowertrain control unit, a transmission control unit, engine controlunit, etc. It should therefore be understood that the control module 28and one or more other controllers can collectively be referred to as a“control module” that controls, such as through a plurality ofintegrated algorithms, various actuators in response to signals fromvarious sensors to control functions associated with the vehicle 10. Inone embodiment, the various controllers that make up the VSC maycommunicate with one another using a common bus protocol (e.g., CAN).

The control module 28 may include a microprocessor or central processingunit (CPU) in communication with various types of computer readablestorage devices or media. Computer readable storage devices or media mayinclude volatile and nonvolatile storage in read-only memory (ROM),random-access memory (RAM), and keep-alive memory (KAM), for example.KAM is a persistent or non-volatile memory that may be used to storevarious operating variables while the CPU is powered down.Computer-readable storage devices or media may be implemented using anyof a number of known memory devices such as PROMs (programmableread-only memory), EPROMs (electrically PROM), EEPROMs (electricallyerasable PROM), flash memory, or any other electric, magnetic, optical,or combination memory devices capable of storing data, some of whichrepresent executable instructions, used by the control module 28 tocontrol the vehicle 10.

The control module 28 may also communicate with various engine/vehiclesensors and actuators via an input/output (I/O) interface that may beimplemented as a single integrated interface that provides various rawdata or signal conditioning, processing, and/or conversion,short-circuit protection, and the like. Alternatively, one or morededicated hardware or firmware chips may be used to condition andprocess particular signals before being supplied to the CPU.

As schematically illustrated in FIG. 1, the control module 28 maycommunicate signals to and/or from the propulsion device 14, thetransmission 16, and the energy source 18. In other words, these devicesare in electrical communication with one another. Although notexplicitly illustrated, those of ordinary skill in the art willrecognize various functions or components that may be controlled by thecontrol module 28 within each of the subsystems identified above.

Of course, the control logic stored on the control module 28 may beimplemented in software, hardware, or a combination of software andhardware in one or more controllers depending upon the particularapplication. When implemented in software, the control logic may beprovided in one or more computer-readable storage devices or mediahaving stored data representing code or instructions executed by acomputer to control the vehicle or its subsystems. The computer-readablestorage devices or media may include one or more of a number of knownphysical devices that utilize electric, magnetic, and/or optical storageto keep executable instructions and associated calibration information,operating variables, and the like.

In one non-limiting embodiment, the control module 28 is configured tocontrol the vehicle 10 based on a learned energy consumption efficiency.The energy consumption efficiency is “learned” in that the controlmodule 28 continuously monitors the energy consumption efficiency (e.g.,miles/gallon, miles/kW-hr, etc.) of the vehicle 10 and selectivelyadapts the energy consumption efficiency based on a detected change inthe energy consumption efficiency. One non-limiting method for learninga vehicle's energy consumption efficiency is described below withrespect to FIG. 2.

FIG. 2, with continued reference to FIG. 1, schematically illustrates acontrol strategy 100 that may be executed by the control module 28 ofthe vehicle 10 for learning an energy consumption efficiency of thevehicle 10. The control module 28 may be programmed to employ one ormore algorithms in order to perform the exemplary control strategy 100.In one non-limiting embodiment, at least equations (1) through (3)(presented below) may be programmed into the control module 28 as partof an algorithm for learning the energy consumption efficiency of thevehicle 10.

The control strategy 100 may represent control logic that is implementedby the control module 28 using hardware, software, or a combination ofhardware and software. For example, the various functions may beperformed using a programmed microprocessor. The control logic may beimplemented using any of a number of known programming or processingtechniques or strategies and is not limited to the order or sequenceillustrated.

In one non-limiting embodiment, the control strategy 100 learns theenergy consumption efficiency of the vehicle 10 in an energy domain. Inother words, the energy consumption efficiency is updated at regularenergy consumption intervals (i.e., every time a predefined amount ofenergy is consumed by the vehicle 10) as opposed to updating in the timedomain at regular time intervals.

The control strategy 100 may begin by integrating a vehicle speed 101(such as in kilometers/hour (kph) or miles/hour (mph)) and a powerconsumption 103 (such as in watts (W) or milligrams of fuel per second(mg/s)) of the vehicle 10 at integrator blocks 102 and 104,respectively. The vehicle speed 101 and the power consumption 103 areknown values. In one embodiment, the vehicle speed 101 and the powerconsumption 103 are measured, sensed and/or calculated by the controlmodule 28 or by some other component(s) that are in communication withthe control module 28 prior to performing the integration at integratorblocks 102, 104.

The power consumption 103 is not necessarily limited to total power ofthe vehicle 10. Power consumption 103 can refer to propulsive power.Other loads like climate loads, accessory loads, etc. can be calculatedin a different domain and then combined with the energy consumptionefficiency of the vehicle 10 as desired.

In other words, although the control strategy 100 is described herein asan energy domain approach, the energy domain approach could be combinedwith a time or distance domain approach.

A distance traveled 105 (such as in kilometers (km) or miles (m)) may beobtained by integrating the vehicle speed 101. In addition, an energyconsumed 107 (such as in kW-hr or gallons of fuel) can be obtained byintegrating the power consumption 103 of the vehicle 10 and scalingbased on application unit conversions. In the discrete time domain, theintegration of the vehicle speed 101 that occurs at integrator block 102may be represented by equation (1), and the integration of the powerconsumption 103 that occurs at integrator block 104 may be representedby equation (2). These equations are presented below:

d _(traveled)(k)=d _(traveled)(k−1) v(k)Δt   (1)

E _(consumed)(k)=E _(consumed)(k−1)+P(k)Δt   (2)

where:

-   -   d_(traveled) is the distance traveled since a previous        integrator reset;    -   E_(consumed) is the energy consumed since a previous integrator        reset;    -   P is the total power consumption;    -   v is the vehicle speed;    -   k is the discrete time index (i.e., an arbitrary value); and    -   Δt is the sampling time of the control module 28.

If the energy consumed 107 since a previous integrator reset is greaterthan or equal to an energy consumed threshold, then the energyconsumption efficiency should be updated. The energy consumed thresholdmay be any threshold, and its actual value could depend on multiplefactors including but not limited to the vehicle type. The controlstrategy 100 ends if the energy consumed threshold has not been exceededby the energy consumed 107. In other words, the estimated energyconsumption efficiency remains constant and is not updated.

However, if the control module 28 determines that the energy consumedthreshold has been exceeded, the integrator blocks 102 and 104 are bothreset by a reset logic block 106. In other words, the reset logic block106 resets the integrator blocks 102, 104 to zero. By way of onenon-limiting embodiment, if the energy consumed exceeds an energyconsumption threshold of 10 W-hr, for example, then the energy consumedand the distance threshold is set to zero such that another 10 W-hr mustbe consumed before the next update. This update is based on the distancetraveled during the interval over which that particular 10 W-hr isconsumed.

The control strategy 100 then proceeds to block 108. At block 108, aratio of the distance traveled 105 to the energy consumed 107 isobtained.

The ratio from block 108 is next filtered at filter block 110. Thefilter block 110 filters out noise and selectively adapts an averageenergy consumption efficiency. In one non-limiting embodiment, for afirst order discrete filter, the energy consumption efficiency can becalculated according to the following equation:

$\begin{matrix}{{\gamma (k)} = \left\{ \begin{matrix}{{\left( {1 - {\tau_{E}^{*}(k)}} \right){\gamma \left( {k - 1} \right)}} + {{\tau_{E}^{*}(k)}{\gamma_{input}(k)}}} & {{{if}\mspace{14mu} E_{traveled}} \geq E_{threshold}} \\{\gamma \left( {k - 1} \right)} & {otherwise}\end{matrix} \right.} & (3)\end{matrix}$

Where:

${{\tau_{E}^{*}(k)} = {\max \left( {{\tau_{E}\frac{E_{traveled}(k)}{E_{threshold}}},1} \right)}};$${{\gamma_{input}(k)} = \frac{d_{traveled}(k)}{E_{consumed}(k)}};$

-   -   γ is the learned energy consumption efficiency;    -   γ_(input) is the energy consumption efficiency of the current        interval;    -   E_(threshold) is the energy consumed threshold; and    -   τ_(E) is the filter constant of the discrete filter in units of        energy (i.e., a filter constant).

The filter block 110 calculates an updated energy consumption efficiency112. The updated energy consumption efficiency 112 can be used tocontrol the vehicle 10 at block 114. For example, the learned energyconsumption efficiency can be used to calculate at least one end usedfeature associated with the vehicle 10. In one non-limiting embodiment,the end use feature includes a distance to empty estimation. However,other end use features are additionally contemplated, including but notlimited to, instantaneous consumption rate display, average consumptionrate/efficiency over the trip odometer, running average consumptionrate/efficiency for the current key cycle, lifetime running averageconsumption rate/efficiency, grade estimation, towing load estimation,energy management, adaptive ECO-routing, ECO-coaching, etc.

In another embodiment, once the updated energy consumption efficiency112 has been calculated or “learned,” an energy consumption rateassociated with the vehicle 10 can also be calculated. The energyconsumption rate is calculated by taking the inverse of the energyconsumption efficiency. Knowing the energy consumption rate can also behelpful for controlling the vehicle 10 or for estimating various end usefeatures associated with the vehicle 10.

Learning the energy consumption efficiency in the energy domain asdetailed above provides an unbiased estimate of energy consumptionefficiency in which an arbitrary learning rate can be chosen. Thus, thecontrol strategy 100 can be used for all applications regardless of timescale requirements.

FIG. 3 schematically illustrates a control strategy 200 according toanother embodiment of this disclosure. In this embodiment, the controlstrategy 200 includes learning an energy consumption efficiency of thevehicle 10 in an energy domain at block 202. For example, the energyconsumption efficiency may be learned by preprocessing a time domaininput and then intelligently triggering an update of an energy domainfilter.

Next, at block 204, the vehicle 10 is controlled using the learnedenergy consumption efficiency. For example, a plurality of end usefeatures may be calculated and displayed to a vehicle driver by usingthe learned energy consumption efficiency.

As shown at block 206, the control strategy 200 can periodically suspendenergy consumption efficiency learning under certain conditions. By wayof one non-limiting embodiment, the control strategy 200 can betemporarily suspended if the vehicle 10 is driving down a steep gradeand it is desired to not have the energy consumption efficiency of thevehicle reflected during such a driving situation. Other conditions mayalso call for suspending the energy consumption efficiency learning. Thecontrol strategy 200 could resume learning at block 208 once normaldriving conditions have been resumed.

Although the different non-limiting embodiments are illustrated ashaving specific components or steps, the embodiments of this disclosureare not limited to those particular combinations. It is possible to usesome of the components or features from any of the non-limitingembodiments in combination with features or components from any of theother non-limiting embodiments.

It should be understood that like reference numerals identifycorresponding or similar elements throughout the several drawings. Itshould be understood that although a particular component arrangement isdisclosed and illustrated in these exemplary embodiments, otherarrangements could also benefit from the teachings of this disclosure.

The foregoing description shall be interpreted as illustrative and notin any limiting sense. A worker of ordinary skill in the art wouldunderstand that certain modifications could come within the scope ofthis disclosure. For these reasons, the following claims should bestudied to determine the true scope and content of this disclosure.

What is claimed is:
 1. A method, comprising: learning an energyconsumption efficiency of a vehicle in an energy domain by periodicallyfiltering a ratio of a distance traveled to an energy consumed, thelearning step executed by a control module configured to monitor theenergy consumption efficiency.
 2. The method as recited in claim 1,wherein the learning step includes monitoring the energy consumption andperiodically adapting an energy consumption efficiency prediction. 3.The method as recited in claim 1, wherein the learning step includes:integrating a speed of the vehicle to obtain the distance traveled; andintegrating a power consumption of the vehicle to obtain the energyconsumed.
 4. The method as recited in claim 3, wherein the filteringstep is selectively performed after the integrating steps.
 5. The methodas recited in claim 3, comprising multiplying the ratio by a filterconstant.
 6. The method as recited in claim 3, comprising updating theenergy consumption efficiency if the energy consumed since a priorintegrator reset is greater than or equal to an energy consumedthreshold.
 7. The method as recited in claim 6, comprising using a priorenergy consumption efficiency if the energy consumed since the priorintegrator reset is not greater than or equal to the energy consumedthreshold.
 8. The method as recited in claim 3, comprising resetting avehicle speed integral and a power consumption integral if the energyconsumed since a prior integrator reset is greater than or equal to anenergy consumed threshold.
 9. The method as recited in claim 1,comprising utilizing the energy consumption efficiency to calculate atleast one end use feature associated with the vehicle.
 10. The method asrecited in claim 9, wherein the at least one end use feature is adistance to empty estimation.
 11. The method as recited in claim 9,wherein the at least one end use feature is an instantaneous consumptionrate display.
 12. The method as recited in claim 9, wherein the at leastone end use feature is at least one of an average consumptionrate/efficiency over a trip odometer, a running average consumptionrate/efficiency for a current key cycle and a lifetime running averageconsumption rate/efficiency.
 13. The method as recited in claim 9,wherein the at least one end use feature is at least one of a gradeestimation and a towing load estimation.
 14. The method as recited inclaim 9, wherein the at least one end use feature is an ECO-routing oran ECO-coaching feature.
 15. The method as recited in claim 1,comprising suspending the learning step during steep grade drivingconditions.
 16. A vehicle, comprising: a transmission; a propulsiondevice coupled to wheels by said transmission; an energy sourceconfigured to power said propulsion device; and a control module inelectrical communication with said propulsion device and said energysource and configured to learn an energy consumption efficiencyassociated with the vehicle in an energy domain.
 17. The vehicle asrecited in claim 16, wherein the control module is configured to updatethe energy consumption efficiency each time a predefined amount ofenergy is consumed by the propulsion device.
 18. The vehicle as recitedin claim 16, wherein the control module is configured to suspendlearning the energy consumption efficiency of the vehicle during steepgrade driving conditions.
 19. The vehicle as recited in claim 16,wherein the control module is configured to: integrate a speed of thevehicle to obtain a distance travelled; and integrate a powerconsumption of the vehicle to obtain an energy consumed.
 20. The vehicleas recited in claim 19, wherein the control module is configured tofilter a ratio of the distance travelled to the energy consumed.